Embodiments of the present invention relate to displays for noise characteristics, especially, to displays of phase noise characteristics adjacent to a desired reference frequency over time.
To evaluate characteristics of a signal generator, such as a PLL synthesizer, it is important to quantify how the output is getting to a stable state after the output frequency setting is changed. To see the transitional characteristics, lockup time, settling characteristics of frequency vs. time, settling characteristics of phase vs. time, etc. have been measured.
The signal generator should ideally provide a signal that has only a set frequency but the actual signal has noise adjacent to the desired frequency. It is referred to as phase noise. The phase noise may lead to system error on a digital communication system, such as a mobile phone system. If an output frequency is changed by frequency hopping, for example, the phase noise characteristics also show transitional change. Therefore it is also important to know how long the characteristics take to get to the stable state for evaluating the characteristics of the signal generator.
One of the conventional inventions for measuring phase noise is disclosed in U.S. Pat. No. 5,412,325 that measures power spectrum density of phase noise by three independent signal sources. However, it is not suitable for measuring transition of phase noise when a desired frequency is changed.
A spectrum analyzer is a representative apparatus for displaying and measuring frequency characteristics of a signal under test. FIG. 3 shows an example of a block diagram of a spectrum analyzer. An analog down converter 22 converts a signal under test into lower frequency signal that is converted into digital time domain data by an analog to digital converter (ADC) 24. A digital down converter converts the time domain data into a further lower frequency, which is converted into frequency domain data by an FFT (fast Fourier transform) calculation block 28. A memory 30 stores the frequency domain data and then a display 32 shows it as graphs, texts, etc. The blocks are coupled to control means that has known microprocessor, hard disk drive, keyboard, etc. (not shown). Program for control may be stored in a mass-storage unit like the hard disk drive.
A modern spectrum analyzer as shown in FIG. 3 depends largely on digital processes to produce spectrums as well as analog processes; it gets time domain data of the signal under test and converts each given number of the data into frequency domain data every predetermined period. Wherein the data unit constituting one spectrum is referred to as a frame and a frequency range derived from the one frame by FFT process is called a frequency span.
FIG. 1 is an example of a spectrum graph showing power versus frequency on a signal under test by spectrum analyzer. If the signal under test is a digital communication signal, a peak 6 may correspond to a carrier frequency and a small peak 8 do to a spurious signal. FIG. 2 is a spectrogram that shows time variation of spectrum characteristics of the signal under test by three dimensions wherein X and Y axes are frequency and time respectively and power is expressed by color variation or grayscale. In FIG. 2, the colors are replaced by monotone patterns. The spectrum waveform of FIG. 1 corresponds to a point in time, or a frame, specified by a marker 16 shown as a dotted line in FIG. 2.
Referring to FIG. 2, the spectrum waveform 12 has the highest power at the center shown as a solid line (that corresponds to the peak 6 of FIG. 1) and the power is getting weaker as it is departing from the center solid line. The waveform 12 has different frequencies on the upper and lower areas of FIG. 2 respectively because it is an example of frequency change by frequency hopping. A spotted pattern 14 shows an example of a spurious signal that corresponds to the small peak 8 shown in FIG. 1.
As for three dimensional spectrum displays, a waterfall display (not shown) is also well known as well as the spectrogram. It uses XYZ axes corresponding to frequency, time and power respectively.
Conventional phase noise characteristics have not been shown as a function of time because the measurement requires a sweep. Therefore the only way for measuring a stabilizing time of the phase noise characteristics is estimation through measurement of lockup time, settling characteristics of frequency versus time, etc.
Now the frequency hopping technology is often used for wireless communication, etc. and it is sometimes required to specify a time point on the phase noise measurement. Therefore what is desired is to express the transitional change in order to easily recognize and quantify the phase noise characteristics when the frequency setting is changed. Besides, it is further desirable to specify a time frame to measure the average, etc. of the phase noise power within the time frame.